In this work, the effects of anisoplanatic turbulence on object detection are analyzed for IR imagery. Object detection is one of the trending challenges of computer vision and deep learning, which is being widely applied in several problem domains [1]}, [2]}. The literature for object detection can be chronologically scrutinized in two mainstream approaches, namely the two-stage and the one-stage object detectors. One-stage approaches such as "YOLO" [3]} and its variants [4]}, [5]}, [6]}, [7]}, are the successors of two-stage approaches and have lower complexities; hence, are popular for real-time applications. Studies on object detection in IR imagery are relatively new [8]}, [9]}, [9]}. Similar architectures to visible domain are being utilized. However, due to relatively low-scales of IR image sets, detection success on IR imagery is lower when compared to visible band. In order to tackle the data problem in IR imagery, recent studies focus on visible-to-IR domain adaptation and achieve satisfactory results using thermal-RGB pairs and supervised learning [11]}, [12]}. Moreover, adversarial-based unsupervised methods are also utilized to thermal domain adaptation [13]}. Hence, thermal adaption is currently a de-facto practice in IR computer vision problems.
